Neural Network Width Scaling for Hyperparameter Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training large neural networks with billions of parameters is computationally expensive and energy-intensive, making multiple tuning and training passes impractical.
Innovation Solution
Tune hyperparameters indirectly on a narrower model and transfer them to the full-size model, leveraging the theory of infinitely wide neural networks to stabilize the hyperparameter landscape across different widths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hyperparameter tuning is performed on a large neural network model, then the model accuracy and performance are improved, but the computational cost and energy consumption increase significantly
Solution Approach 1:
The patent creates a simplified copy (narrower model) of the large neural network for hyperparameter tuning. This narrower model has the same architecture and training procedure but with reduced width, allowing efficient hyperparameter optimization without the full computational cost of training on the large model, thus resolving the contradiction between model accuracy and energy consumption
Solution Approach 2:
The patent segments the hyperparameter tuning process from the full model training process. By separating the optimization of hyperparameters (which can be done on a narrow model) from the actual training of the large model, the system reduces energy consumption while maintaining the ability to achieve high accuracy through proper hyperparameter selection
2Reliability
If multiple tuning and training passes are performed on large models, then the model performance is optimized, but the computational resources and time required become impractical
Solution Approach 1:
The patent uses a narrower model as a computational surrogate that can be quickly tuned through multiple passes. The hyperparameter optimization is performed on this fast-to-train narrow model rather than the computationally expensive large model, enabling multiple tuning passes to be performed efficiently and improving overall productivity
Solution Approach 2:
The patent performs preliminary hyperparameter optimization on the narrow model before training the large model. This preliminary action identifies optimal hyperparameter settings that can then be applied to the large model training, reducing the need for multiple expensive training passes and improving computational efficiency
3Use of energy by moving object
If hyperparameter tuning is performed on a narrower model, then the computational cost is reduced, but the accuracy of the tuned hyperparameters may be compromised
Solution Approach 1:
The patent changes the width parameter of the neural network to create a narrower model for hyperparameter tuning. By adjusting this structural parameter, the system achieves lower computational cost while the hyperparameter optimization process remains valid, as the narrow model shares the same architectural principles and training dynamics as the large model
Solution Approach 2:
The narrower model is designed as a structural copy of the large model, preserving the same architecture, loss function, and training procedure. This copying ensures that hyperparameters optimized on the narrow model are transferable to the large model, maintaining measurement precision while reducing computational cost
Data Source
AI summary
Systems and method are provided that are directed to tuning a hyperparameter associated with a small neural network model and transferring the hyperparameter to a large neural network model. At least one neural network model may be received along with a request for one or more tuned hyperparameters. Prior to scaling the large neural network, the large neural network is parameterized in accordance with a parameterizing scheme. The large neural network is then scaled and reduced in size such that a hyperparameter tuning process may be performed. A tuned hyperparameter may then be provided to a requestor such that the hyperparameter can be directly input into the large neural network. By tuning a hyper parameter using a small neural network, significant computation cycles and energy may be saved.